import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from einops import rearrange
from timm.models.layers import DropPath, to_2tuple, trunc_normal_

class DoubleConv(nn.Module):
    """同UNet定义连续的两次卷积"""

    def __init__(self, in_channel, out_channel):
        super(DoubleConv, self).__init__()
        # 俩次卷积
        self.d_conv = nn.Sequential(
            nn.Conv2d(in_channel, out_channel, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(out_channel),
            nn.ReLU(inplace=True),

            nn.Conv2d(out_channel, out_channel, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(out_channel),
            nn.ReLU(inplace=True),
        )

    def forward(self, x):
        return self.d_conv(x)


class MoEFFNGating(nn.Module):
    def __init__(self, dim, hidden_dim, num_experts):
        super(MoEFFNGating, self).__init__()
        self.gating_network = nn.Linear(dim, dim)
        self.experts = nn.ModuleList([nn.Sequential(
            nn.Linear(dim, hidden_dim),
            nn.GELU(),
            nn.Linear(hidden_dim, dim)) for _ in range(num_experts)])

    def forward(self, x):
        weights = self.gating_network(x)
        weights = torch.nn.functional.softmax(weights, dim=-1)
        outputs = [expert(x) for expert in self.experts]
        outputs = torch.stack(outputs, dim=0)
        outputs = (weights.unsqueeze(0) * outputs).sum(dim=0)
        return outputs


class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


def window_partition(x, window_size):
    """
    Args:
        x: (B, H, W, C)
        window_size (int): window size

    Returns:
        windows: (num_windows*B, window_size, window_size, C)
    """
    B, H, W, C = x.shape
    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
    return windows


def window_reverse(windows, window_size, H, W):
    """
    Args:
        windows: (num_windows*B, window_size, window_size, C)
        window_size (int): Window size
        H (int): Height of image
        W (int): Width of image

    Returns:
        x: (B, H, W, C)
    """
    B = int(windows.shape[0] / (H * W / window_size / window_size))
    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
    return x


class WindowAttention(nn.Module):
    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
    It supports both of shifted and non-shifted window.

    Args:
        dim (int): Number of input channels.
        window_size (tuple[int]): The height and width of the window.
        num_heads (int): Number of attention heads.
        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
    """

    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):

        super().__init__()
        self.dim = dim
        self.window_size = window_size  # Wh, Ww
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5

        # define a parameter table of relative position bias
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH

        # get pair-wise relative position index for each token inside the window
        coords_h = torch.arange(self.window_size[0])
        coords_w = torch.arange(self.window_size[1])
        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
        relative_coords[:, :, 1] += self.window_size[1] - 1
        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
        self.register_buffer("relative_position_index", relative_position_index)

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        trunc_normal_(self.relative_position_bias_table, std=.02)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x, mask=None):
        """
        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
        """
        B_, N, C = x.shape
        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)

        q = q * self.scale
        attn = (q @ k.transpose(-2, -1))

        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
        attn = attn + relative_position_bias.unsqueeze(0)

        if mask is not None:
            nW = mask.shape[0]
            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, N, N)
            attn = self.softmax(attn)
        else:
            attn = self.softmax(attn)

        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x

    def extra_repr(self) -> str:
        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'

    def flops(self, N):
        # calculate flops for 1 window with token length of N
        flops = 0
        # qkv = self.qkv(x)
        flops += N * self.dim * 3 * self.dim
        # attn = (q @ k.transpose(-2, -1))
        flops += self.num_heads * N * (self.dim // self.num_heads) * N
        #  x = (attn @ v)
        flops += self.num_heads * N * N * (self.dim // self.num_heads)
        # x = self.proj(x)
        flops += N * self.dim * self.dim
        return flops


class SwinTransformerBlock(nn.Module):
    r""" Swin Transformer块。
    参数：
        dim (int): 输入通道的数量。
        input_resolution (tuple[int]): 输入分辨率。
        num_heads (int): 注意力头的数量。
        window_size (int): 窗口大小。
        shift_size (int): SW-MSA的移位大小。
        mlp_ratio (float): MLP隐层维度与嵌入维度的比率。
        qkv_bias (bool, 可选): 如果为True，则向查询、键、值添加可学习的偏置。默认值：True。
        qk_scale (float | None, 可选): 如果设置，则覆盖默认的qk缩放，默认为head_dim ** -0.5。
        drop (float, 可选): Dropout比率。默认值：0.0。
        attn_drop (float, 可选): 注意力机制中的Dropout比率。默认值：0.0。
        drop_path (float, 可选): 随机深度的比率。默认值：0.0。
        act_layer (nn.Module, 可选): 激活层。默认值：nn.GELU。
        norm_layer (nn.Module, 可选): 归一化层。默认值：nn.LayerNorm。
    """

    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        if min(self.input_resolution) <= self.window_size:
            # if window size is larger than input resolution, we don't partition windows
            self.shift_size = 0
            self.window_size = min(self.input_resolution)
        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"

        self.norm1 = norm_layer(dim)
        self.attn = WindowAttention(
            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)

        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

        if self.shift_size > 0:
            # calculate attention mask for SW-MSA
            H, W = self.input_resolution
            img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
            h_slices = (slice(0, -self.window_size),
                        slice(-self.window_size, -self.shift_size),
                        slice(-self.shift_size, None))
            w_slices = (slice(0, -self.window_size),
                        slice(-self.window_size, -self.shift_size),
                        slice(-self.shift_size, None))
            cnt = 0
            for h in h_slices:
                for w in w_slices:
                    img_mask[:, h, w, :] = cnt
                    cnt += 1

            mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
            mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
            attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
        else:
            attn_mask = None

        self.register_buffer("attn_mask", attn_mask)

    def forward(self, x):
        H, W = self.input_resolution
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"

        shortcut = x
        x = self.norm1(x)
        x = x.view(B, H, W, C)

        # cyclic shift
        if self.shift_size > 0:
            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
        else:
            shifted_x = x

        # partition windows
        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C

        # W-MSA/SW-MSA
        attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C

        # merge windows
        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C

        # reverse cyclic shift
        if self.shift_size > 0:
            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
        else:
            x = shifted_x
        x = x.view(B, H * W, C)

        # FFN
        x = shortcut + self.drop_path(x)
        x = x + self.drop_path(self.mlp(self.norm2(x)))

        return x

    def extra_repr(self) -> str:
        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"

    def flops(self):
        flops = 0
        H, W = self.input_resolution
        # norm1
        flops += self.dim * H * W
        # W-MSA/SW-MSA
        nW = H * W / self.window_size / self.window_size
        flops += nW * self.attn.flops(self.window_size * self.window_size)
        # mlp
        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
        # norm2
        flops += self.dim * H * W
        return flops


class PatchMerging(nn.Module):
    r""" Patch Merging Layer.

    Args:
        input_resolution (tuple[int]): Resolution of input feature.
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(4 * dim)

    def forward(self, x):
        """
        x: B, H*W, C
        """
        H, W = self.input_resolution
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"
        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."

        x = x.view(B, H, W, C)

        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C

        x = self.norm(x)
        x = self.reduction(x)

        return x

    def extra_repr(self) -> str:
        return f"input_resolution={self.input_resolution}, dim={self.dim}"

    def flops(self):
        H, W = self.input_resolution
        flops = H * W * self.dim
        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
        return flops


class PatchExpand(nn.Module):
    def __init__(self, input_resolution, dim, dim_scale=2, norm_layer=nn.LayerNorm):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.expand = nn.Linear(dim, 2 * dim, bias=False) if dim_scale == 2 else nn.Identity()
        self.norm = norm_layer(dim // dim_scale)

    def forward(self, x):
        """
        x: B, H*W, C
        """
        H, W = self.input_resolution
        x = self.expand(x)
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"

        x = x.view(B, H, W, C)
        x = rearrange(x, 'b h w (p1 p2 c)-> b (h p1) (w p2) c', p1=2, p2=2, c=C // 4)
        x = x.view(B, -1, C // 4)
        x = self.norm(x)

        return x


class FinalPatchExpand_X4(nn.Module):
    def __init__(self, input_resolution, dim, dim_scale=4, norm_layer=nn.LayerNorm):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.dim_scale = dim_scale
        self.expand = nn.Linear(dim, 16 * dim, bias=False)
        self.output_dim = dim
        self.norm = norm_layer(self.output_dim)

    def forward(self, x):
        """
        x: B, H*W, C
        """
        H, W = self.input_resolution
        x = self.expand(x)
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"

        x = x.view(B, H, W, C)
        x = rearrange(x, 'b h w (p1 p2 c)-> b (h p1) (w p2) c', p1=self.dim_scale, p2=self.dim_scale,
                      c=C // (self.dim_scale ** 2))
        x = x.view(B, -1, self.output_dim)
        x = self.norm(x)

        return x


class BasicLayer(nn.Module):
    """ A basic Swin Transformer layer for one stage.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        depth (int): Number of blocks.
        num_heads (int): Number of attention heads.
        window_size (int): Local window size.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
    """

    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):

        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth
        self.use_checkpoint = use_checkpoint

        # build blocks
        self.blocks = nn.ModuleList([
            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
                                 num_heads=num_heads, window_size=window_size,
                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
                                 mlp_ratio=mlp_ratio,
                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
                                 drop=drop, attn_drop=attn_drop,
                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                                 norm_layer=norm_layer)
            for i in range(depth)])

        # patch merging layer
        if downsample is not None:
            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
        else:
            self.downsample = None

    def forward(self, x):
        for blk in self.blocks:
            if self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x)
            else:
                x = blk(x)
        if self.downsample is not None:
            x = self.downsample(x)
        return x

    def extra_repr(self) -> str:
        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"

    def flops(self):
        flops = 0
        for blk in self.blocks:
            flops += blk.flops()
        if self.downsample is not None:
            flops += self.downsample.flops()
        return flops


class BasicLayer_up(nn.Module):
    """ A basic Swin Transformer layer for one stage.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        depth (int): Number of blocks.
        num_heads (int): Number of attention heads.
        window_size (int): Local window size.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
        upsample (nn.Module | None, optional): upsample layer at the end of the layer. Default: None
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
    """

    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., norm_layer=nn.LayerNorm, upsample=None, use_checkpoint=False):

        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth
        self.use_checkpoint = use_checkpoint

        # build blocks
        self.blocks = nn.ModuleList([
            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
                                 num_heads=num_heads, window_size=window_size,
                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
                                 mlp_ratio=mlp_ratio,
                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
                                 drop=drop, attn_drop=attn_drop,
                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                                 norm_layer=norm_layer)
            for i in range(depth)])

        # patch merging layer
        if upsample is not None:
            self.upsample = PatchExpand(input_resolution, dim=dim, dim_scale=2, norm_layer=norm_layer)
        else:
            self.upsample = None

    def forward(self, x):
        for blk in self.blocks:
            if self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x)
            else:
                x = blk(x)
        if self.upsample is not None:
            x = self.upsample(x)
        return x


class PatchEmbed(nn.Module):
    r""" Image to Patch Embedding

    Args:
        img_size (int): Image size.  Default: 224.
        patch_size (int): Patch token size. Default: 4.
        in_chans (int): Number of input image channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        norm_layer (nn.Module, optional): Normalization layer. Default: None
    """

    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
        self.img_size = img_size
        self.patch_size = patch_size
        self.patches_resolution = patches_resolution
        self.num_patches = patches_resolution[0] * patches_resolution[1]

        self.in_chans = in_chans
        self.embed_dim = embed_dim

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = None

    def forward(self, x):
        B, C, H, W = x.shape
        # FIXME look at relaxing size constraints
        assert H == self.img_size[0] and W == self.img_size[1], \
            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
        x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C
        if self.norm is not None:
            x = self.norm(x)
        return x

    def flops(self):
        Ho, Wo = self.patches_resolution
        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
        if self.norm is not None:
            flops += Ho * Wo * self.embed_dim
        return flops


class SwinTansformerUnetPP(nn.Module):
    r""" Swin Transformer
        这是 Swin Transformer 的 PyTorch 实现
        Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
        https://arxiv.org/pdf/2103.14030
        参数说明：
            img_size (int | tuple(int)): 输入图像的尺寸。默认值为 224。
            patch_size (int | tuple(int)): 图像切片的大小。默认值为 4。
            in_chans (int): 输入图像的通道数。默认值为 3（即RGB图像）。
            num_classes (int): 分类头的类别数。默认值为 1000。
            embed_dim (int): 图像切片嵌入维度。默认值为 96。
            depths (tuple(int)): 每一层 Swin Transformer 的深度。
            num_heads (tuple(int)): 不同层的注意力头的数量。
            window_size (int): 窗口大小。默认值为 7。
            mlp_ratio (float): MLP 层隐藏维度与嵌入维度的比值。默认值为 4。
            qkv_bias (bool): 如果为 True，将会为查询（query）、键（key）和值（value）添加可学习的偏置项。默认值为 True。
            qk_scale (float): 如果设置，将覆盖默认的 qk 缩放因子（即 head_dim ** -0.5）。默认值为 None。
            drop_rate (float): Dropout 比例。默认值为 0。
            attn_drop_rate (float): 注意力的 Dropout 比例。默认值为 0。
            drop_path_rate (float): 随机深度（Stochastic depth）率。默认值为 0.1。
            norm_layer (nn.Module): 标准化层。默认值为 nn.LayerNorm。
            ape (bool): 如果为 True，将在图像切片嵌入中添加绝对位置嵌入。默认值为 False。
            patch_norm (bool): 如果为 True，切片嵌入后将添加标准化。默认值为 True。
            use_checkpoint (bool): 是否使用检查点（checkpointing）来节省内存。默认值为 False。
    """

    def __init__(self,
                 # 图像类别
                 # 默认224
                 img_size=224,
                 # 编码器块的数量
                 patch_size=4,
                 # 输入通道数
                 in_chans=3,
                 # 类别数
                 num_classes=1000,
                 # 图像切片嵌入维度
                 embed_dim=96,
                 # 编码器中每层的深度，即每个阶段包含的块（Block）的数量。
                 # 默认为 [2, 2, 2, 2]
                 depths=[2, 2, 2, 2],
                 # 解码器每层的深度，即每个阶段包含的块（Block）的数量
                 depths_decoder=[1, 2, 2, 2],
                 # 不同层的注意力头的数量
                 num_heads=[3, 6, 12, 24],
                 # 窗口大小。
                 # 默认值为 7
                 window_size=7,
                 # MLP 层隐藏维度与嵌入维度的比值。
                 # 默认值为 4。
                 mlp_ratio=4.,
                 # 如果为 True，将会为查询（query）、键（key）和值（value）添加可学习的偏置项。
                 # 默认值为 True。
                 qkv_bias=True,
                 # 如果设置，将覆盖默认的 qk 缩放因子（即 head_dim ** -0.5）。
                 # 默认值为 None。
                 qk_scale=None,
                 # Dropout 比例。
                 # 默认值为 0。
                 drop_rate=0.,
                 # 注意力的 Dropout 比例。
                 # 默认值为 0。
                 attn_drop_rate=0.,
                 # 随机深度（Stochastic depth）率。
                 # 默认值为 0.1。
                 drop_path_rate=0.1,
                 # 归一化层
                 # 默认是 LayerNorm
                 norm_layer=nn.LayerNorm,
                 # 如果为 True，将在图像切片嵌入中添加绝对位置嵌入。
                 # 默认值为 False。
                 ape=False,
                 # 如果为 True，切片嵌入后将添加标准化。
                 # 默认值为 True。
                 patch_norm=True,
                 # 是否使用检查点（checkpointing）来节省内存。
                 # 默认值为 False。
                 use_checkpoint=False,
                 # 最终上采样层的逻辑
                 final_upsample="expand_first",
                 **kwargs):
        super().__init__()

        print(
            "SwinTransformerSys expand initial----depths:{};depths_decoder:{};drop_path_rate:{};num_classes:{}".format(
                depths,
                depths_decoder, drop_path_rate, num_classes))
        self.num_classes = num_classes
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.ape = ape
        self.patch_norm = patch_norm
        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
        self.num_features_up = int(embed_dim * 2)
        self.mlp_ratio = mlp_ratio
        self.final_upsample = final_upsample

        '''初始切片层'''
        # 将图片批号进行切片
        self.patch_embed = PatchEmbed(
            img_size=img_size,
            patch_size=patch_size,
            in_chans=in_chans,
            embed_dim=embed_dim,
            norm_layer=norm_layer if self.patch_norm else None)
        num_patches = self.patch_embed.num_patches
        patches_resolution = self.patch_embed.patches_resolution
        self.patches_resolution = patches_resolution
        # 绝对位置切片
        if self.ape:
            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
            trunc_normal_(self.absolute_pos_embed, std=.02)
        self.pos_drop = nn.Dropout(p=drop_rate)
        # 每个阶段的深度 
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
        '''原始下采样层'''
        self.layer1_0 = BasicLayer(
            dim=int(embed_dim * 2 ** 0),
            input_resolution=(patches_resolution[0] // (2 ** 0),
                              patches_resolution[1] // (2 ** 0)),
            depth=depths[0],
            num_heads=num_heads[0],
            window_size=window_size,
            mlp_ratio=self.mlp_ratio,
            qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate,
            drop_path=dpr[sum(depths[:0]):sum(depths[:0 + 1])],
            norm_layer=norm_layer,
            downsample=PatchMerging if (0 < self.num_layers - 1) else None,
            use_checkpoint=use_checkpoint
        )
        self.layer2_0 = BasicLayer(
            dim=int(embed_dim * 2 ** 1),
            input_resolution=(patches_resolution[0] // (2 ** 1),
                              patches_resolution[1] // (2 ** 1)),
            depth=depths[1],
            num_heads=num_heads[1],
            window_size=window_size,
            mlp_ratio=self.mlp_ratio,
            qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate,
            drop_path=dpr[sum(depths[:1]):sum(depths[:1 + 1])],
            norm_layer=norm_layer,
            downsample=PatchMerging if (1 < self.num_layers - 1) else None,
            use_checkpoint=use_checkpoint
        )
        self.layer3_0 = BasicLayer(
            dim=int(embed_dim * 2 ** 2),
            input_resolution=(patches_resolution[0] // (2 ** 2),
                              patches_resolution[1] // (2 ** 2)),
            depth=depths[2],
            num_heads=num_heads[2],
            window_size=window_size,
            mlp_ratio=self.mlp_ratio,
            qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate,
            drop_path=dpr[sum(depths[:2]):sum(depths[:2 + 1])],
            norm_layer=norm_layer,
            downsample=PatchMerging if (2 < self.num_layers - 1) else None,
            use_checkpoint=use_checkpoint
        )
        self.layer4_0 = BasicLayer(
            dim=int(embed_dim * 2 ** 3),
            input_resolution=(patches_resolution[0] // (2 ** 3),
                              patches_resolution[1] // (2 ** 3)),
            depth=depths[3],
            num_heads=num_heads[3],
            window_size=window_size,
            mlp_ratio=self.mlp_ratio,
            qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate,
            drop_path=dpr[sum(depths[:3]):sum(depths[:3 + 1])],
            norm_layer=norm_layer,
            downsample=PatchMerging if (3 < self.num_layers - 1) else None,
            use_checkpoint=use_checkpoint
        )
        '''原始上采样层'''
        self.layer4_1 = PatchExpand(
            input_resolution=(patches_resolution[0] // (2 ** (self.num_layers - 1 - 0)),
                              patches_resolution[1] // (2 ** (self.num_layers - 1 - 0))),
            dim=int(embed_dim * 2 ** (self.num_layers - 1 - 0)), dim_scale=2, norm_layer=norm_layer)
        self.layer3_1 = BasicLayer_up(
            dim=int(embed_dim * 2 ** (self.num_layers - 1 - 1)),
            input_resolution=(patches_resolution[0] // (2 ** (self.num_layers - 1 - 1)),
                              patches_resolution[1] // (2 ** (self.num_layers - 1 - 1))),
            depth=depths[(self.num_layers - 1 - 1)],
            num_heads=num_heads[(self.num_layers - 1 - 1)],
            window_size=window_size,
            mlp_ratio=self.mlp_ratio,
            qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate,
            drop_path=dpr[sum(depths[:(self.num_layers - 1 - 1)]):sum(
                depths[:(self.num_layers - 1 - 1) + 1])],
            norm_layer=norm_layer,
            upsample=PatchExpand if (1 < self.num_layers - 1) else None,
            use_checkpoint=use_checkpoint)
        self.layer2_2 = BasicLayer_up(
            dim=int(embed_dim * 2 ** (self.num_layers - 1 - 2)),
            input_resolution=(patches_resolution[0] // (2 ** (self.num_layers - 1 - 2)),
                              patches_resolution[1] // (2 ** (self.num_layers - 1 - 2))),
            depth=depths[(self.num_layers - 1 - 2)],
            num_heads=num_heads[(self.num_layers - 1 - 2)],
            window_size=window_size,
            mlp_ratio=self.mlp_ratio,
            qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate,
            drop_path=dpr[sum(depths[:(self.num_layers - 1 - 2)]):sum(
                depths[:(self.num_layers - 1 - 2) + 1])],
            norm_layer=norm_layer,
            upsample=PatchExpand if (2 < self.num_layers - 1) else None,
            use_checkpoint=use_checkpoint)
        self.layer1_3 = BasicLayer_up(
            dim=int(embed_dim * 2 ** (self.num_layers - 1 - 3)),
            input_resolution=(patches_resolution[0] // (2 ** (self.num_layers - 1 - 3)),
                              patches_resolution[1] // (2 ** (self.num_layers - 1 - 3))),
            depth=depths[(self.num_layers - 1 - 3)],
            num_heads=num_heads[(self.num_layers - 1 - 3)],
            window_size=window_size,
            mlp_ratio=self.mlp_ratio,
            qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate,
            drop_path=dpr[sum(depths[:(self.num_layers - 1 - 3)]):sum(
                depths[:(self.num_layers - 1 - 3) + 1])],
            norm_layer=norm_layer,
            upsample=PatchExpand if (3 < self.num_layers - 1) else None,
            use_checkpoint=use_checkpoint)
        '''原始线性层'''
        self.concat_linear0 = nn.Linear(
            2 * int(embed_dim * 2 ** (self.num_layers - 1 - 0)),
            int(embed_dim * 2 ** (self.num_layers - 1 - 0))) if 0 > 0 else nn.Identity()
        self.concat_linear1 = nn.Linear(
            2 * int(embed_dim * 2 ** (self.num_layers - 1 - 1)),
            int(embed_dim * 2 ** (self.num_layers - 1 - 1))) if 1 > 0 else nn.Identity()
        self.concat_linear2 = nn.Linear(
            2 * int(embed_dim * 2 ** (self.num_layers - 1 - 2)),
            int(embed_dim * 2 ** (self.num_layers - 1 - 2))) if 2 > 0 else nn.Identity()
        self.concat_linear3 = nn.Linear(
            2 * int(embed_dim * 2 ** (self.num_layers - 1 - 3)),
            int(embed_dim * 2 ** (self.num_layers - 1 - 3))) if 3 > 0 else nn.Identity()
        '''最终展开层'''
        if self.final_upsample == "expand_first":
            print("---final upsample expand_first---")
            self.up = FinalPatchExpand_X4(input_resolution=(img_size // patch_size, img_size // patch_size),
                                          dim_scale=4, dim=embed_dim)
            self.output = nn.Conv2d(in_channels=embed_dim, out_channels=self.num_classes, kernel_size=1, bias=False)
        # 初始化权重
        self.apply(self._init_weights)
        # 归一化
        self.norm = norm_layer(self.num_features)  # 用于对编码器输出的最后一个特征图进行归一化
        self.norm_up = norm_layer(self.embed_dim)  # 用于对解码器输出的最后一个特征图进行归一化

    def _init_weights(self, m: nn.Module):
        # print(m, getattr(getattr(m, "weight", nn.Identity()), "INIT", None), isinstance(m, nn.Linear), "======================")
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'absolute_pos_embed'}

    @torch.jit.ignore
    def no_weight_decay_keywords(self):
        return {'relative_position_bias_table'}

    def up_x4(self, x):
        H, W = self.patches_resolution
        B, L, C = x.shape
        assert L == H * W, "input features has wrong size"
        # 判断是否需要执行上采样
        if self.final_upsample == "expand_first":
            # 执行上采样
            x = self.up(x)
            #  调整特征图形状
            x = x.view(B, 4 * H, 4 * W, -1)
            # 通过 permute 调整维度顺序
            x = x.permute(0, 3, 1, 2)  # B,C,H,W
            # 最终输出
            x = self.output(x)
        return x

    def flops(self):
        flops = 0
        flops += self.patch_embed.flops()
        for i, layer in enumerate(self.layers):
            flops += layer.flops()
        flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers)
        flops += self.num_features * self.num_classes
        return flops

    def forward(self, x):
        x_downsample = []
        # 原始下擦偶啊漫画侧面和
        x = self.patch_embed(x)
        if self.ape:
            x = x + self.absolute_pos_embed
        x = self.pos_drop(x)
        # 下采样+特征提取
        x_downsample.append(x)
        x = self.layer1_0(x)
        # 下采样+特征提取
        x_downsample.append(x)
        x = self.layer2_0(x)
        # 下采样+特征提取
        x_downsample.append(x)
        x = self.layer3_0(x)
        # 下采样+特征提取
        x_downsample.append(x)
        x = self.layer4_0(x)
        # 编码器归一化
        x = self.norm(x)  # B L C

        # 上采样
        x = self.layer4_1(x)
        # 上采样+特征融合
        x = torch.cat([x, x_downsample[2]], -1)
        x = self.concat_linear1(x)
        x = self.layer3_1(x)
        # 上采样+特征融合
        x = torch.cat([x, x_downsample[1]], -1)
        x = self.concat_linear2(x)
        x = self.layer2_2(x)
        # 上采样+特征融合
        x = torch.cat([x, x_downsample[0]], -1)
        x = self.concat_linear3(x)
        x = self.layer1_3(x)
        # 解码器归一化
        x = self.norm_up(x)  # B L C
        # 最终上采样层
        x = self.up_x4(x)

        return x


if __name__ == "__main__":
    model = SwinTansformerUnetPP().to('cuda')
    a = torch.randn(24, 3, 224, 224).cuda()
    out = model(a)
    print(out.size())
